Learning interestingness of streaming classification rules

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dc.citation.epage71en_US
dc.citation.spage62en_US
dc.citation.volumeNumber3280en_US
dc.contributor.authorAydın, Tolgaen_US
dc.contributor.authorGüvenir, Halil Altayen_US
dc.coverage.spatialKemer-Antalya, Turkeyen_US
dc.date.accessioned2016-02-08T10:25:07Z
dc.date.available2016-02-08T10:25:07Zen_US
dc.date.issued2004en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 27-29 October 2004en_US
dc.descriptionConference name: ISCIS 200419th International Symposiumen_US
dc.description.abstractInducing classification rules on domains from which information is gathered at regular periods lead the number of such classification rules to be generally so huge that selection of interesting ones among all discovered rules becomes an important task. At each period, using the newly gathered information from the domain, the new classification rules are induced. Therefore, these rules stream through time and are so called streaming classification rules. In this paper, an interactive rule interestingness-learning algorithm (IRIL) is developed to automatically label the classification rules either as "interesting" or "uninteresting" with limited user interaction. In our study, VFP (Voting Feature Projections), a feature projection based incremental classification learning algorithm, is also developed in the framework of IRIL. The concept description learned by the VFP algorithm constitutes a novel approach for interestingness analysis of streaming classification rules. © Springer-Verlag 2004.en_US
dc.identifier.doi10.1007/978-3-540-30182-0_7en_US
dc.identifier.doi10.1007/b101749en_US
dc.identifier.isbn978 3 540 30182 0en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/24169en_US
dc.language.isoEnglishen_US
dc.publisherSpringer, Berlin, Heidelbergen_US
dc.relation.isversionofhttps://doi.org/10.1007/978-3-540-30182-0_7en_US
dc.relation.isversionofhttps://doi.org/10.1007/b101749en_US
dc.source.titleComputer and Information Sciences - ISCIS 2004en_US
dc.subjectInterestingness factoren_US
dc.subjectClassification ruleen_US
dc.subjectTraining instanceen_US
dc.subjectNominal featureen_US
dc.subjectAction abilityen_US
dc.titleLearning interestingness of streaming classification rulesen_US
dc.typeConference Paperen_US
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